Process documents with recursive chunking using Google Drive, OpenAI & Gemini RAG

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Important notice

This workflow is provided as-is. Please review and test before using in production.

1. Workflow Overview

1. Document Ingestion & Processing Google Drive Trigger monitors for new files → Loop Over Items processes each file → File Info extracts metadata → Google Drive downloads the actual content → Swit...

Best for

Internal Wiki automation workflows
AI RAG automation workflows
advanced n8n builders looking for reusable templates

Tools used

n8n-nodes-base.googledrivetrigger, n8n-nodes-base.splitinbatches, n8n-nodes-base.googledrive, n8n-nodes-base.switch, @n8n/n8n-nodes-langchain.chainllm, @n8n/n8n-nodes-langchain.lmchatopenai, n8n-nodes-base.summarize, @n8n/n8n-nodes-langchain.embeddingsopenai

Source and attribution

This workflow is cataloged by N8N Workflows and links back to its original n8n.io source page by Mohsin Ali.

Original n8n.io source

1.1 Workflow description

Title: Process documents with recursive chunking using Google Drive, OpenAI & Gemini RAG
Workflow name: Process documents with recursive chunking using Google Drive, OpenAI & Gemini RAG

1. Document Ingestion & Processing

Google Drive Trigger monitors for new files → Loop Over Items processes each file → File Info extracts metadata → Google Drive downloads the actual content → Switch routes to appropriate extractors (PDF or TEXT) based on file type

2. Content Transformation & Chunking

Document Data node processes extracted text → Recursive Splitter breaks content into contextual chunks → Chunk Splitting applies intelligent segmentation while preserving document context and relationships between chunks

3. Embedding & Storage

Basic LLM Chain processes chunks → OpenAI Chat Model generates contextual understanding → Summarize creates document summaries → Supabase Vector Store saves embeddings with metadata → Embeddings OpenAI creates vector representations → Default Data Loader handles storage operations

4. Query Processing & Retrieval

When Clicking Execute triggers user queries → OpenAI processes and understands the question → AI Agent orchestrates hybrid search (combining vector similarity + keyword matching) → Google Gemini Chat Model generates final responses using retrieved context → HTTP Request handles additional external data sources

1.2 Logical Blocks

This catalog entry is organized from the workflow JSON. The node-level section below shows the executable blocks available for review before importing the template.

2. Block-by-Block Analysis

Block 1 - Google Drive Trigger

Type / Role: n8n-nodes-base.googleDriveTrigger - googleDriveTrigger
Config choices: Version 1

Block 2 - Loop Over Items

Type / Role: n8n-nodes-base.splitInBatches - splitInBatches
Config choices: Version 3

Block 3 - Google Drive

Type / Role: n8n-nodes-base.googleDrive - googleDrive
Config choices: Version 3

Block 4 - Switch

Type / Role: n8n-nodes-base.switch - switch
Config choices: Version 3.2

Block 5 - Basic LLM Chain

Type / Role: @n8n/n8n-nodes-langchain.chainLlm - chainLlm
Config choices: Version 1.7

Block 6 - OpenAI Chat Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatOpenAi - lmChatOpenAi
Config choices: Version 1.2

Block 7 - Summarize

Type / Role: n8n-nodes-base.summarize - summarize
Config choices: Version 1.1

Block 8 - Embeddings OpenAI

Type / Role: @n8n/n8n-nodes-langchain.embeddingsOpenAi - embeddingsOpenAi
Config choices: Version 1.2

Block 9 - Supabase Vector Store

Type / Role: @n8n/n8n-nodes-langchain.vectorStoreSupabase - vectorStoreSupabase
Config choices: Version 1.2

Block 10 - Default Data Loader

Type / Role: @n8n/n8n-nodes-langchain.documentDefaultDataLoader - documentDefaultDataLoader
Config choices: Version 1

Block 11 - Character Text Splitter

Type / Role: @n8n/n8n-nodes-langchain.textSplitterCharacterTextSplitter - textSplitterCharacterTextSplitter
Config choices: Version 1

Block 12 - AI Agent

Type / Role: @n8n/n8n-nodes-langchain.agent - agent
Config choices: Version 2

Block 13 - Sticky Note

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 14 - Sticky Note1

Type / Role: n8n-nodes-base.stickyNote - stickyNote
Config choices: Version 1

Block 15 - When clicking ‘Execute workflow’

Type / Role: n8n-nodes-base.manualTrigger - manualTrigger
Config choices: Version 1

Block 16 - Google Gemini Chat Model

Type / Role: @n8n/n8n-nodes-langchain.lmChatGoogleGemini - lmChatGoogleGemini
Config choices: Version 1

Block 17 - OpenAI

Type / Role: @n8n/n8n-nodes-langchain.openAi - openAi
Config choices: Version 1.8

Block 18 - File info

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 19 - Extract from PDF

Type / Role: n8n-nodes-base.extractFromFile - extractFromFile
Config choices: Version 1

Block 20 - Extract from TEXT

Type / Role: n8n-nodes-base.extractFromFile - extractFromFile
Config choices: Version 1

Block 21 - PDF to DATA

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 22 - Recursive Splitter

Type / Role: n8n-nodes-base.code - code
Config choices: Version 2

Block 23 - Document Data

Type / Role: n8n-nodes-base.set - set
Config choices: Version 3.4

Block 24 - Chunk Splitting

Type / Role: n8n-nodes-base.splitOut - splitOut
Config choices: Version 1

Showing the first 24 of 25 workflow blocks. Download the JSON for the full node graph.

3. Summary Table

Workflow	Process documents with recursive chunking using Google Drive, OpenAI & Gemini RAG
Complexity	advanced
Nodes	25
Categories	Internal Wiki, AI RAG
Author	Mohsin Ali
Published	01 Jul 2025

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

Use the JSON export at /data/workflows/5521/5521.json as the source template for this automation.
2. Import the template into n8n

Open n8n, import the downloaded JSON, and review each node before activating the workflow.
3. Configure credentials and variables

Replace placeholder credentials, API keys, webhook URLs, account IDs, and environment-specific values with your own settings.
4. Test with sample data

Run the workflow manually or in a staging workspace, inspect node output, and confirm downstream systems receive the expected data.
5. Activate and monitor

Enable the workflow only after testing, then monitor executions, errors, and rate limits during the first production runs.

5. General Notes & Resources

Review imported nodes carefully before activation. This catalog entry is intended to help you inspect the workflow structure, understand required services, and find related templates faster.

Node names, credentials, schedules, webhook paths, and external service limits may need adjustment for your workspace.

Download workflow JSON Original n8n.io source Internal Wiki workflows AI RAG workflows

Frequently asked questions

What does Process documents with recursive chunking using Google Drive, OpenAI & Gemini RAG do?

What do I need before importing this workflow?

Review the workflow JSON, configure any required credentials in n8n, and test the automation in a safe workspace before using it in production.

Can I customize this workflow?

Yes. Use the block-by-block analysis and the downloadable JSON to inspect each node, then adjust credentials, prompts, schedules, filters, or destinations for your Internal Wiki, AI RAG use case.

Mohsin Ali

1 workflow

Nodes

n8n-nodes-base.googledrivetrigger n8n-nodes-base.splitinbatches n8n-nodes-base.googledrive n8n-nodes-base.switch @n8n/n8n-nodes-langchain.chainllm @n8n/n8n-nodes-langchain.lmchatopenai n8n-nodes-base.summarize @n8n/n8n-nodes-langchain.embeddingsopenai

Complexity

advanced

Published 01 Jul 2025

Likes 0

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Install path: /data/workflows/5521/5521.json

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Process documents with recursive chunking using Google Drive, OpenAI & Gemini RAG

Workflow preview

Important notice

1. Workflow Overview

Best for

Tools used

Source and attribution

1.1 Workflow description

1.2 Logical Blocks

2. Block-by-Block Analysis

Block 1 - Google Drive Trigger

Block 2 - Loop Over Items

Block 3 - Google Drive

Block 4 - Switch

Block 5 - Basic LLM Chain

Block 6 - OpenAI Chat Model

Block 7 - Summarize

Block 8 - Embeddings OpenAI

Block 9 - Supabase Vector Store

Block 10 - Default Data Loader

Block 11 - Character Text Splitter

Block 12 - AI Agent

Block 13 - Sticky Note

Block 14 - Sticky Note1

Block 15 - When clicking ‘Execute workflow’

Block 16 - Google Gemini Chat Model

Block 17 - OpenAI

Block 18 - File info

Block 19 - Extract from PDF

Block 20 - Extract from TEXT

Block 21 - PDF to DATA

Block 22 - Recursive Splitter

Block 23 - Document Data

Block 24 - Chunk Splitting

3. Summary Table

4. Reproducing the Workflow from Scratch

1. Download the workflow JSON

2. Import the template into n8n

3. Configure credentials and variables

4. Test with sample data

5. Activate and monitor

5. General Notes & Resources

Frequently asked questions